Towards Semantic Detection of Smells in Cloud Infrastructure Code.

Indika Kumara, Zoe Vasileiou, Georgios Meditskos, Damian A. Tamburri, Willem-Jan van den Heuvel, Anastasios Karakostas, Stefanos Vrochidis, Ioannis Kompatsiaris

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

6 Citaten (Scopus)

Samenvatting

Automated deployment and management of Cloud applications relies on descriptions of their deployment topologies, often referred to as Infrastructure Code. As the complexity of applications and their deployment models increases, developers inadvertently introduce software smells to such code specifications, for instance, violations of good coding practices, modular structure, and more. This paper presents a knowledge-driven approach enabling developers to identify the aforementioned smells in deployment descriptions. We detect smells with SPARQL-based rules over pattern-based OWL 2 knowledge graphs capturing deployment models. We show the feasibility of our approach with a prototype and three case studies.

Originele taal-2Engels
TitelWIMS 2020: Proceedings of the 10th International Conference on Web Intelligence, Mining and Semantics
Pagina's63-67
Aantal pagina's5
DOI's
StatusGepubliceerd - 2020

Bibliografische nota

DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

Vingerafdruk

Duik in de onderzoeksthema's van 'Towards Semantic Detection of Smells in Cloud Infrastructure Code.'. Samen vormen ze een unieke vingerafdruk.

Citeer dit